Near infrared chemical imaging microscope

ABSTRACT

A chemical imaging system is provided which uses a near infrared radiation microscope. The system includes an illumination source which illuminates an area of a sample using light in the near infrared radiation wavelength and light in the visible wavelength. A multitude of spatially resolved spectra of transmitted, reflected, emitted or scattered near infrared wavelength radiation light from the illuminated area of the sample is collected and a collimated beam is produced therefrom. A near infrared imaging spectrometer is provided for selecting a near infrared radiation image of the collimated beam. The filtered images are collected by a detector for further processing. The visible wavelength light from the illuminated area of the sample is simultaneously detected providing for the simultaneous visible and near infrared chemical imaging analysis of the sample. Two efficient means for performing three dimensional near infrared chemical imaging microscopy are provided.

This application claims the benefit of U.S. Provisional Application No.60/239,969, entitled “Near Infrared Chemical Imaging Microscope” filedOct. 13, 2000.

This work is supported by the National Institute of Standards andTechnology (NIST) under the Advanced Technology Program (ATP) award(Contract Number 70NANB8H4021)

FIELD OF INVENTION

The present invention is related to near-infrared (NIR) microscopes forspectroscopic and image analysis, and, in particular, to microscopesuseful for both NIR spectroscopy, NIR chemical imaging and NIRvolumetric chemical imaging.

BACKGROUND OF THE INVENTION

NIR spectroscopy is a mature, non-contact, non-destructive analyticalcharacterization tool that has been widely applied to a broad range ofmaterials. The NIR region of the electromagnetic spectrum encompassesradiation with wavelengths of 0.78 to 2.5 μm (12,800 to 4,000 cm⁻¹). NIRspectra result from the overtone and combination bands of fundamentalmid-infrared (MIR) bands. Among the many desirable characteristics, NIRis used to rapidly obtain both qualitative and quantitative informationabout the molecular makeup of a material. Digital imaging, on the otherhand, provides a means to obtain optical (i.e., spatial—morphological,topographical, etc.) information about a material. By combining thespatial information obtained from digital imagery and the spectralinformation obtained from NIR spectroscopy, the chemical makeup ofcomplex material matrices can be mapped out in both two and threespatial dimensions. NIR chemical imaging combines NIR spectroscopy anddigital imaging for the molecular-specific analysis of materials. A NIRchemical imaging microscope apparatus employing NIR absorption molecularspectroscopy for materials characterization is disclosed.

State-of-the-Art Instrumentation

NIR microscopes are used to obtain NIR absorption, transmittance orreflectance spectra (e.g., NIR microspectra) from samples ranging insize between 1 and 1000 μm. These instruments are typically equippedwith a digital camera to visually locate a region of interest on asample upon which a NIR light beam from a Fourier transform (FT)spectrometer is focused. Reflective optics are used to direct thetransmitted or reflected light from the sample to a NIR detector. Theoutput is a NIR absorption spectrum collected in transmittance orreflectance mode.

NIR chemical imaging can be considered an extension of NIRmicrospectroscopy. Much of the imaging performed since the developmentof the first NIR microprobes has involved spatial scanning of samplesbeneath NIR microscopes in order to construct NIR “maps” of surfaces. Inpoint by point scanning with NIR microscopes, the NIR light beam isfocused onto the surface of a sample or apertured to illuminate a smallregion of a sample and a spectrum from each spatial position iscollected. Images are obtained by rastering the sample through thefocused or apertured NIR light beam and the spectra recorded are thenreconstructed to form an image. Although point scanning produces imagesbased on NIR contrast, long experimental times are common since theduration of the experiment is proportional to the number of imagepixels. As a direct result, point scan images are captured at low imagedefinition, which relates directly to the limited utility of thetechnique as an imaging tool for the routine assessment of materialmorphology. The spatial resolution of the image is limited by the sizeof the NIR illumination spot on the sample (no less than 1 μm) and therastering mechanism, which requires the use of moving mechanical partsthat are challenging to operate reproducibly.

NIR imaging cameras have been used in photography for decades. Untilrecently, however, it has not been easily accessible to those not versedin traditional photographic processes. By using optical filters (e.g.,cold filters) that block the visible wavelengths (0.4-0.78 μm),charge-coupled devices (CCDs) used in digital cameras and camcorders canbe used to sense NIR light out to around 1100 nm. Other regions of theNIR spectrum can be viewed using devices such as indium gallium arsenide(InGaAs—0.9 μm to 1.7 μm) and indium antimonide (InSb—1.0 μm to 5.0 μm)focal plane array (FPA) detectors. These integrated wavelength NIRimaging approaches allow one to study relative light intensities ofobjects over broad ranges of the NIR spectrum, but useful chemicalinformation is unattainable without the use of some type of discretewavelength filtering device.

The use of dielectric interference filters in combination with NIR FPAsis one method in which chemical information can be obtained from asample. To form NIR chemical images, a NIR light beam is defocused toilluminate a wide field of view and the reflected or transmitted lightfrom the illuminated area is imaged onto a two-dimensional NIR detector.A selection of discrete dielectric interference filters provided in afilter wheel, or a linearly variable or circularly variable format canbe positioned in front of a broadband NIR light source, or in front ofthe NIR FPA itself in order to collect NIR wavelength resolved images.Typically, the use of several fixed bandpass filters is required toaccess the entire NIR spectrum. The spatial resolution of the NIR imageapproaches that of the optical microscope, while spectral resolution ofseveral nanometers has been demonstrated. Key limitations of thedielectric filter approach include the need for a multitude of discretefilters to provide appreciable free spectral range, or the reliance onmoving mechanical parts in employing continuously tunable dielectricinterference filters as a requirement to form wavelength resolvedimages. While moving mechanical assemblies can be engineered they addcost and complexity to NIR chemical imaging systems. Alternatives tomoving mechanical assemblies are generally more cost effective andprovide performance advantages.

Acousto-optic tunable filters (AOTFs) have been employed asno-moving-parts imaging spectrometers for NIR imaging. The AOTF is asolid-state device that is capable of functioning from the UV to themid-IR depending on the choice of the filter's crystal material.Operation of the AOTF is based on the interaction of light with atraveling acoustic sound wave in an anisotropic crystal medium. Theincident light is diffracted with a narrow spectral bandpass when an rfsignal is applied to the device. By changing the applied rf frequencyunder computer control the spectral passband can be tuned rapidly withthe benefit of non-moving parts.

For use in NIR chemical imaging, AOTFs have distinct limitations. AOTFshave imaging performance that is degraded appreciably fromdiffraction-limited conditions due to dispersion effects and imageshifting effects. Furthermore, AOTFs suffer from temperature instabilityand exhibit nonlinear properties that complicate their use as imagingspectrometers.

An aim of NIR chemical imaging technology development has been todevelop a NIR imaging technique that combines diffraction-limitedspatial resolution with high spectral resolution. NIR chemical imagingtechniques have only recently achieved a degree of technologicalmaturity that allow the collection of high resolution (spectral andspatial) data with the advent of the liquid crystal (LC) imagingspectrometers. In general, LC devices provide diffraction-limitedspatial resolution. The spectral resolution of the LC imagingspectrometer is comparable to that provided by dispersive monochromatorand Fourier transform interferometers. In addition, LC technologyprovides high out of band rejection, broad free spectral range, moderatetransmittance, high overall etendue and highly reproducible randomaccess computer controlled tuning.

Under normal NIR imaging operation, LC imaging spectrometers allow NIRchemical images of samples to be recorded at discrete wavelengths(energies). A spectrum is generated corresponding to thousands ofspatial locations at the sample surface by tuning the LC imagingspectrometer over a range of wavelengths and collecting NIR imagessystematically. Contrast is generated in the images based on therelative amounts of NIR absorption, transmittance or reflectance that isgenerated by the different species located throughout the sample. Sincea high quality NIR spectrum is generated for each pixel location, a widevariety of chemometric analysis tools, both univariate and multivariate,can be applied to the NIR image data to extract pertinent information.Correlative multivariate routines are particularly powerful when appliedto chemical images collected from samples intentionally seeded with aknown standard material. This approach of incorporating calibrationstandards within an image field of view can be extended to quantitativechemical image analysis. In addition, digital image analysis procedurescan also be applied to high image quality NIR chemical images to performroutine particle analysis in both two (2D) and three (3D) spatialdimensions. Volumetric 3D NIR chemical image analysis can be performedvery effectively using numerical deconvolution computational strategies.

SUMMARY OF THE INVENTION

To address the need for a device that can provide video imaging, NIRspectroscopy and high resolution (spatial and spectral) NIR chemicalimaging in two and three spatial dimensions, a novel NIR chemicalimaging microscope has been developed that is NIR chemical imagingcapable.

The microscope design uses NIR optimized liquid crystal (LC) imagingspectrometer technology for wavelength selection. The NIR optimizedrefractive microscope is used in conjunction with infinity-correctedobjectives to form the NIR image on the detector with or without the useof a tube lens. An integrated parfocal analog color CCD detectorprovides real-time sample positioning and focusing. The color image andthe NIR image are fused in software. In one configuration, the NIRmicroscope may be used as a volumetric imaging instrument through themeans of moving the sample through focus, collecting images at varyingfocal depths and reconstructing a volumetric image of the sample insoftware, or through the means of keeping the sample fixed and changingthe wavelength dependent depth of penetration in conjunction with arefractive tube lens with a well characterized chromatic effect. Theoutput of the microscope can be coupled to a NIR spectrometer either viadirect optical coupling or via a fiber optic. A Chemical ImagingAddition Method seeds the sample with a material of known composition,structure and/or concentration and then generates the NIR image suitablefor qualitative and quantitative analysis. The microscope generates NIRchemical image data that is analyzed and visualized using chemical imageanalysis software in a systematic and comprehensive manner. While thisinvention has been demonstrated on a microscope optic platform, thenovel concepts are also applicable to other image gathering platforms,namely fiberscopes, macrolens systems and telescopes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic diagram of the near-infrared (NIR) chemicalimaging microscope

FIG. 2 shows a diagram of the chemical imaging data analysis cycleperformed in software.

FIG. 3 is a digital brightfield image of a CdZnTe semiconductor materialdecorated with tellurium inclusions.

FIG. 4 an NIR microscopic transmittance image of a CdZnTe semiconductormaterial decorated with tellurium inclusions.

FIG. 5A illustrates a raw NIR image frame of a CdZnTe wafer sample.

FIG. 5B illustrates an NIR image frame of the sample of FIG. 5A in whichthe threshold value for the image was set too low.

FIG. 5C illustrates an NIR image frame of the sample of FIG. 5A in whichthe threshold value for the image was set too high.

FIG. 5D illustrates an NIR image frame of the sample of FIG. 5A in whichthe threshold value for the image was set to an intermediate level.

FIG. 6A is the original raw image of four adjacent regions of intereston a CdZnTe wafer.

FIG. 6B is the background-corrected image corresponding to the fouradjacent regions of interest of the CdZnTe wafer of FIG. 6A.

FIG. 6C is the binarized image corresponding to the four adjacentregions of interest of the CdZnTe wafer of FIG. 6A.

FIG. 7 is a three-dimensional view of tellurium inclusions in a CdZnTewafer.

DETAILED DESCRIPTION OF THE INVENTION

The NIR chemical imaging microscope combines in a single platform a NIRoptimized refractive optical microscope base, which is equipped with NIRoptimized infinity-corrected microscope objectives, an automated XYZtranslational microscope stage and quartz tungsten halogen (QTH) lampsto secure and illuminate samples for NIR spectroscopy and imaging, ananalog color charge-coupled device (CCD) detector for ordinary opticalimage collection and digital image collection, a NIR LC imagingspectrometer for NIR chemical image wavelength selection and a roomtemperature or optionally cooled NIR FPA for NIR image capture.

FIG. 1 is a schematic diagram of the NIR chemical imaging microscope.NIR illumination is directed to the sample in a reflected lightconfiguration using a QTH source or other broadband white light source,including metal halide or Xe arc lamps 1 or a transmitted lightconfiguration using QTH or suitable NIR source 2 of an NIR optimizedrefractive optical microscope platform 3. The reflected or transmittedNIR light is collected from the sample positioned on the automated XYZtranslational microscope stage 4 through an infinity-corrected NIRoptimized microscope objective 5.

Ordinary optical imagery of the sample can be obtained using a mirror orbeamsplitter or prism arrangement inserted into turret 6 and collectingan image with an analog or digital color or monochrome charge-coupleddevice (CCD) or CMOS detector 7. In NIR chemical imaging mode, themagnified NIR image is coupled through a NIR LC imaging spectrometer 8and collected on a room temperature or cooled NIR focal plane array(FPA) detector 9. The FPA is typically comprised of indium galliumarsenide (InGaAs), but may be comprised of other NIR sensitivematerials, including platinum silicide (PtSi), indium antimonide (InSb)or mercury cadmium telluride (HgCdTe). Using a beamsplitting elementinserted into turret 6, NIR and ordinary optical imagery can becollected with an analog monochrome or color CCD detector 7 and NIR FPA9 simultaneously.

A central processing unit 10, typically a Pentium computer, is used forNIR chemical image collection and processing. The analog color CCD 7,NIR FPA 9, automated XYZ translational microscope stage 4 controlled viaa controller 12 and NIR LC imaging spectrometer 8 (through LC imagingspectrometer controller 11) are operated with commercial software, suchas Acquisition Manager (Chemlcon Inc.) in conjunction with Chemlmage(Chemlcon Inc.).

By introducing a polarization sensitive beam splitting element in theoptical path prior to the NIR LC imaging spectrometer 8 (not shown inschematic diagram), a portion of the NIR light from the sample may becoupled to a remote NIR spectrometer (also not shown in schematicdiagram).

Preferably, NIR optimized liquid crystal (LC) imaging spectrometertechnology is used for wavelength selection. The LC imaging spectrometermay be of the following types: Lyot liquid crystal tunable filter(LCTF); Evans Split-Element LCTF; Solc LCTF; Ferroelectric LCTF; Liquidcrystal Fabry Perot (LCFP); or a hybrid filter technology comprised of acombination of the above-mentioned LC filter types or the abovementioned filter types in combination with fixed bandbass and bandrejectfilters comprised of dielectric, rugate, holographic, color absorption,acousto-optic or polarization types.

One novel component of this invention, is that a NIR optimizedrefractive microscope is used in conjunction with infinity-correctedobjectives to form the NIR image on the detector without the use of atube lens. The microscope can be optimized for NIR operation throughinherent design of objective and associated anti-reflective coatings,condenser and light source. To simultaneously provide high numericalapertures the objective should be refractive. To minimize chromaticaberration, maximize throughput and reduce cost the conventional tubelens can be eliminated, while having the NIR objective form the NIRimage directly onto the NIR focal plane array (FPA) detector, typicallyof the InGaAs type. The FPA can also be comprised of Si, SiGe, PtSi,InSb, HgCdTe, PdSi, Ge, analog vidicon types. The FPA output isdigitized using an analog or digital frame grabber approach.

An integrated parfocal analog CCD detector provides real-time samplepositioning and focusing. An analog video camera sensitive to visibleradiation, typically a color or monochrome CCD detector, but may becomprised of a CMOS type, is positioned parfocal with the NIR FPAdetector to facilitate sample positioning and focusing without requiringdirect viewing of the sample through conventional eyepieces. The videocamera output is typically digitized using a frame grabber approach.

The color image and the NIR image are fused using software. While theNIR and visible cameras often generate images having differing contrast,the sample fields of view can be matched through a combination ofoptical and software manipulations. As a result, the NIR and visibleimages can be compared and even fused through the use of overlaytechniques and correlation techniques to provide the user a near-realtime view of both detector outputs on the same computer display. Thecomparitive and integrated views of the sample can significantly enhancethe understanding of sample morphology and architecture. By comparingthe visible, NIR and NIR chemical images, additional useful informationcan be acquired about the chemical composition, structure andconcentration of species in samples.

The NIR microscope can be used as a volumetric imaging instrumentthrough the means of moving the sample through focus in the Z, axialdimension, collecting images in and out of focus and reconstructing avolumetric image of the sample in software. For samples having somevolume (bulk materials, surfaces, interfaces, interphases), volumetricchemical imaging in the NIR has been shown to be useful for failureanalysis, product development and routine quality monitoring. Thepotential also exists for performing quantitative analysis simultaneouswith volumetric analysis. Volumetric imaging can be performed in anon-contact mode without modifying the sample through the use ofnumerical confocal techniques, which require that the sample be imagedat discrete focal planes. The resulting images are processed andreconstructed and visualized. Computional optical sectioningreconstruction techniques based on a variety of strategies have beendemonstrated, including nearest neighbors and iterative deconvolution.

An alternative to sample positioning combined with computationreconstruction is to employ a tube lens in the image formation path ofthe microscope which introduces chromatic aberration. As a result thesample can be interrogated as a function of sample depth by exercisingthe LC imaging spectrometer, collecting images at different wavelengthswhich penetrate to differing degrees into bulk materials. Thesewavelength dependent, depth dependent images can be reconstructed toform volumetric images of materials without requiring the sample to bemoved, again through application of computational optical sectioningreconstruction algorithms.

The output of the microscope can be coupled to a NIR spectrometer eithervia direct optical coupling or via a fiber optic cable. This allowsconventional spectroscopic tools to be used to gather NIR spectra fortraditional, high speed spectral analysis. The spectrometers can be ofthe following types: fixed filter spectrometers; grating basedspectrometers; Fourier Transform spectrometers; or Acousto-Opticspectrometers.

A novel method that is readily employed by the disclosed microscopeinvention is a method described as the Chemical Imaging Addition Methodwhich involves seeding the sample with a material of known composition,structure and/or concentration and then generating the NIR imagesuitable for qualitative and quantitative analysis. The Chemical ImagingAddition Method is a novel extension of a standard analytical chemicalanalysis technique, the Standard Addition Method. A common practice inquantitative chemical analysis is to construct a standard calibrationcurve which is a plot of analytical response for a particular techniqueas a function of known analyte concentration. By measuring theanalytical response from an unknown sample, an estimate of the analyteconcentration can then be extrapolated from the calibration curve. Inthe Standard Addition Method, known quantities of the analyte are addedto the samples and the increase in analytical response is measured. Whenthe analytical response is linearly related to concentration, theconcentration of the unknown analyte can be found by plotting theanalytical response from a series of standards and extrapolating theunknown concentration from the curve. In this graph, however, the x-axisis the concentration of added analyte after being mixed with the sample.The x-intercept of the curve is the concentration of the unknownfollowing dilution. The primary advantage of the standard additionmethod is that the matrix remains constant for all samples.

While the Standard Addition Method is used specifically for quantitativeanalysis, the Chemical Imaging Addition Method can be used forqualitative and quantitative analysis. The Chemical Imaging AdditionMethod relies upon spatially isolating analyte standards in order tocalibrate the Chemical Imaging analysis. In chemical imaging, thousandsof linearly independent, spatially-resolved spectra are collected inparallel of analytes found within complex host matrices. These spectracan then be processed to generate unique contrast intrinsic to analytespecies without the use of stains, dyes, or contrast agents. Variousspectroscopic methods including near-infrared (NIR) absorptionspectroscopy can be used to probe molecular composition and structurewithout being destructive to the sample. Similarly, in NIR chemicalimaging the contrast that is generated reveals the spatial distributionof properties revealed in the underlying NIR spectra.

The Chemical Imaging Addition Method can involve several data processingsteps, typically including, but not limited to:

1. Ratiometric correction in which the sample NIR image is divided bythe background NIR image to produce a result having a floating pointdata type.

2. The divided image is normalized by dividing each intensity value atevery pixel in the image by the vector norm for its corresponding pixelspectrum. Where the vector norm is the square root of the sum of thesquares of pixel intensity values for each pixel spectrum. Normalizationis applied for qualitative analysis of NIR chemical images. Forquantitative analysis, normalization is not employed, but relies insteadon the use of partial least squares regression (PLSR) techniques.

3. Correlation analysis, including Euclidian Distance and Cosinecorrelation analysis (CCA) are established multivariate image analysistechniques that assess similarity in spectral image data whilesimultaneously suppressing background effects. More specifically, CCAassesses chemical heterogeneity without the need for training sets,identifies differences in spectral shape and efficiently provideschemical image based contrast that is independent of absolute intensity.The CCA algorithm treats each pixel spectrum as a projected vector inn-dimensional space, where n is the number of wavelengths sampled in theimage. An orthonormal basis set of vectors is chosen as the set ofreference vectors and the cosine of the angles between each pixelspectrum vector and the reference vectors are calculated. The intensityvalues displayed in the resulting CCA images are these cosine values,where a cosine value of 1 indicates the pixel spectrum and referencespectrum are identical, and a cosine value of 0 indicates the pixelspectrum and the reference spectrum are orthogonal (no correlation). Thedimensions of the resulting CCA image is the same as the original imagebecause the orthonormal basis set provides n reference vectors,resulting in n CCA images.

4. Principal component analysis (PCA) is a data space dimensionalityreduction technique. A least squares fit is drawn through the maximumvariance in the n-dimensional dataset. The vector resulting from thisleast squares fit is termed the first principal component (PC) or thefirst loading. After subtracting the variance explained from the firstPC, the operation is repeated and the second principal component iscalculated. This process is repeated until some percentage of the totalvariance in the data space is explained (normally 95% or greater). PCScore images can then be visualized to reveal orthogonal informationincluding sample information, as well as instrument response, includingnoise. Reconstruction of spectral dimension data can then be performedguided by cluster analysis, including without PCs that describe materialor instrument parameters that one desires to amplify or suppress,depending on the needs of the sensing application.

Effective materials characterization with the disclosed NIR chemicalimaging microscope invention typically requires application of amultitude of software procedures to the NIR chemical image. A schematicof the chemical image analysis cycle is shown in FIG. 2. A fairlycomprehensive description of the variety of steps used to processchemical images is described below.

Until recently, seamless integration of spectral analysis, chemometricanalysis and digital image analysis has not been commercially available.Individual communities have independently developed advanced softwareapplicable to their specific requirements. For example, digital imagingsoftware packages that treat single-frame gray-scale images and spectralprocessing programs that apply chemometric techniques have both reacheda relatively mature state. One limitation to the development of chemicalimaging, however, has been the lack of integrated software that combinesenough of the features of each of these individual disciplines to havepractical utility.

Historically, practitioners of chemical imaging were forced to developtheir own software routines to perform each of the key steps of the dataanalysis. Typically, routines were prototyped using packages thatsupported scripting capability, such as Matlab, IDL, Grams or LabView.These packages, while flexible, are limited by steep learning curves,computational inefficiencies, and the need for individual practitionersto develop their own graphical user interface (GUI). Today, commerciallyavailable software does exist that provides efficient data processingand the ease of use of a simple GUI.

Software that meets these goals must address the entirety of thechemical imaging process. The chemical imaging analysis cycleillustrates the steps needed to successfully extract information fromchemical images and to tap the full potential provided by chemicalimaging systems. The cycle begins with the selection of samplemeasurement strategies and continues through to the presentation of ameasurement solution. The first step is the collection of images. Therelated software must accommodate the full complement of chemical imageacquisition configurations, including support of various spectroscopictechniques, the associated spectrometers and imaging detectors, and thesampling flexibility required by differing sample sizes and collectiontimes. Ideally, even relatively disparate instrument designs can haveone intuitive GUI to facilitate ease of use and ease of adoption.

The second step in the analysis cycle is data preprocessing. In general,preprocessing steps attempt to minimize contributions from chemicalimaging instrument response that are not related to variations in thechemical composition of the imaged sample. Some of the functionalitiesneeded include: correction for detector response, including variationsin detector quantum efficiency, bad detector pixels and cosmic events;variation in source illumination intensity across the sample; and grossdifferentiation between spectral lineshapes based on baseline fittingand subtraction. Examples of tools available for preprocessing includeratiometric correction of detector pixel response; spectral operationssuch as Fourier filters and other spectral filters, normalization, meancentering, baseline correction, and smoothing; spatial operations suchas cosmic filtering, low-pass filters, high-pass filters, and a numberof other spatial filters.

Once instrument response has been suppressed, qualitative processing canbe employed. Qualitative chemical image analysis attempts to address asimple question, “What is present and how is it distributed?”. Manychemometric tools fall under this category. A partial list includes:correlation techniques such as cosine correlation and Euclidean distancecorrelation; classification techniques such as principal componentsanalysis, cluster analysis, discriminant analysis, and multi-wayanalysis; and spectral deconvolution techniques such as SIMPLISMA,linear spectral unmixing and multivariate curve resolution.

Quantitative analysis deals with the development of concentration mapimages. Just as in quantitative spectral analysis, a number ofmultivariate chemometric techniques can be used to build the calibrationmodels. In applying quantitative chemical imaging, all of the challengesexperienced in non-imaging spectral analysis are present in quantitativechemical imaging, such as the selection of the calibration set and theverification of the model. However, in chemical imaging additionalchallenges exist, such as variations in sample thickness and thevariability of multiple detector elements, to name a few. Depending onthe quality of the models developed, the results can range fromsemi-quantitative concentration maps to rigorous quantitativemeasurements.

Results obtained from preprocessing, qualitative analysis andquantitative analysis must be visualized. Software tools must providescaling, automapping, pseudo-color image representation, surface maps,volumetric representation, and multiple modes of presentation such assingle image frame views, montage views, and animation ofmultidimensional chemical images, as well as a variety of digital imageanalysis algorithms for look up table (LUT) manipulation and contrastenhancement.

Once digital chemical images have been generated, traditional digitalimage analysis can be applied. For example, Spatial Analysis andChemical Image Measurement involve binarization of the high bit depth(typically 32 bits/pixel) chemical image using threshold andsegmentation strategies. Once binary images have been generated,analysis tools can examine a number of image domain features such assize, location, alignment, shape factors, domain count, domain density,and classification of domains based on any of the selected features.Results of these calculations can be used to develop key quantitativeimage parameters that can be used to characterize materials.

The final category of tools, Automated Image Processing, involves theautomation of key steps or of the entire chemical image analysisprocess. For example, the detection of well defined features in an imagecan be completely automated and the results of these automated analysescan be tabulated based on any number of criteria (particle size, shape,chemical composition, etc). Automated chemical imaging platforms havebeen developed that can run for hours in an unsupervised fashion.

This invention incorporates a comprehensive analysis approach thatallows user's to carefully plan experiments and optimize instrumentparameters and should allow the maximum amount of information to beextracted from chemical images so that the user can make intelligentdecisions.

EXAMPLE

Overview

As the demand for high quality, low cost X-ray, γ-ray and imagingdetector devices increases, there is a need to improve the quality andproduction yield of semiconductor materials used in these devices. Oneeffective strategy for improving semiconductor device yield is throughthe use of better device characterization tools that can rapidly andnondestructively identify defects at early stages in the fabricationprocess. Early screening helps to elucidate the underlying causes ofdefects and to reduce downstream costs associated with processing defectladen materials that are ultimately scrapped. The present invention canbe used to characterize tellurium inclusion defects in cadmium zinctelluride (CdZnTe) semiconductor materials based on near infraredimaging. With this approach, large area wafers can be inspected rapidlyand non-destructively in two and three spatial dimensions by collectingNIR image frames at multiple regions of interest throughout the waferusing an automated NIR imaging system. The NIR image frames aresubjected to image processing algorithms including background correctionand image binarization. Particle analysis is performed on the binarizedimages to reveal tellurium inclusion statistics, sufficient to pass orfail wafers. In addition, data visualization software is used to viewthe tellurium inclusions in two and three spatial dimensions.

Background

The present invention has been used to automatically inspect telluriuminclusions in CdZnTe. Compound semiconductors are challenging tofabricate. There are several steps along the manufacturing process inwhich defects can arise. The chemical nature associated withsemiconductor defects often plays a vital role in device performance.Device fabrication and device processing defects can be difficult andtime consuming to measure during manufacturing. Unfortunately, defectivedevices are often left undiagnosed until latter stages in themanufacturing process because of the inadequacy of the metrology toolsbeing used. This results in low production yields and high costs whichcan be an impediment to growth in the semiconductor device marketpotential.

There is a general need in the semiconductor industry for metrologytechnologies that can nondestructively assess semiconductor materialdefects and ultimately increase manufacturing yields. A potentialsolution is to develop a high throughput screening system capable offusing multiple chemical imaging modalities into a single instrument.Chemical imaging combines digital imaging and molecular spectroscopy forthe chemical analysis of materials. A modality of based on near-infrared(NIR) chemical imaging can be used to inspect tellurium inclusions inCdZnTe compound semiconductor materials.

CdZnTe is a leading material for use in room temperature X-raydetectors, γ-ray radiation detectors and imaging devices. Applicationsfor these devices include nuclear diagnostics, digital radiography,high-resolution astrophysical X-ray and γ-ray imaging, industrial webgauging and nuclear nonproliferation. These devices are often decoratedwith microscopic and macroscopic defects limiting the yield oflarge-size, high-quality materials. Defects commonly found in thesematerials include cracks, grain boundaries, twin boundaries, pipes,precipitates and inclusions. CdZnTe wafers are often graded based on thesize and number of Te inclusion defects present.

The definition used by Rudolph and Muhlberg for tellurium inclusions(i.e., tellurium-rich domains in the 1-50 μm size range that originateas a result of morphological instabilities at the growth interface astellurium-rich melt droplets are captured from the boundary layer aheadof the interface) has been adopted and is used herein. There have beennumerous studies on the composition and distribution of telluriuminclusions in CdZnTe material. It has been demonstrated that thepresence of tellurium inclusions can impair the electronic properties ofCdZnTe materials—consequently degrading the end-product deviceperformance.

The current procedure used by low volume semiconductor manufacturers forcharacterizing tellurium inclusions in CdZnTe is labor intensive,susceptible to human error and provides little information on inclusionsin the 1-5 μm size scale. Inclusions are viewed and counted manually bya human operator using an IR microscope platform. When an inclusion isidentified that is suspected to exceed a specified size limit, aPolaroid film photograph is taken. An overlay of a stage micrometer islaid over the photograph to determine the size. This analysis isrelatively time consuming, often taking several minutes to characterizea region of interest from a large wafer.

The present invention can be used for automated characterization ofmicroscale tellurium inclusions in CdZnTe based on volumetric NIRchemical imaging. The system takes advantage of the fact that CdZnTe istransparent to infrared wavelengths (>850 nm). When viewing CdZnTe withan infrared focal plane array (IR-FPA) through a NIR LC imagingspectrometer, tellurium inclusions appear as dark, absorbing domains.The invention images wafers in two and three spatial dimensionscapturing raw infrared images at each region of interest. Images areautomatically background equilibrated, binarized and processed. Theprocessed data provides particle statistical information such asinclusion counts, sizes, density, area and shape. The system provides arapid method for characterizing tellurium inclusions as small as 0.5 μmwhile virtually eliminating the subjectivity associated with manualinspection.

Sample Description

Tellurium-rich CdZnTe samples were produced by a commercial supplier (eVProducts) for analysis. Samples containing high tellurium inclusiondensities were purposely acquired to effectively demonstrate thecapabilities of the automated tellurium inclusions mapping system. TheCdZnTe materials were grown by the Horizontal Bridgeman (HB) method andcontained a nominal zinc cation loading concentration of 4% and anaverage etch pit density of 4×10⁴/cm². The materials displayed a face A<111> orientation and were polished on both sides. Sample thicknessesranged from approximately 1 mm to 15 mm. No further sample preparationwas necessary for the automated tellurium inclusion mapping analysis.

Data Collection

Volumetric maps of the tellurium inclusions in the CdZnTe samples wereobtained by first placing the sample on the XYZ-translational stage ofthe automated mapping system. NIR image frames were then capturedthrough the LC imaging spectrometer at a wavelength that maximized theTe precipitate contrast relative to the surrounding CdZnTe matrix in theX-Y direction at multiple regions of interest across the samples. Depthprofiling was achieved by translating the sample focus under themicroscope at user-defined increments. This process was then repeated inan iterative fashion until the entire wafer was characterized.

Data Processing

Once imaging data was collected, ChemImage was used to process the data.For each wafer, the software generates a background-corrected grayscaleimage, a binarized image using the threshold value selected for eachframe of the image, a montage view of the binarized image and particlestatistics. The particle statistics table includes information such asparticle counts, particle sizes, particles densities, and a number ofgeometrical parameters such as particle area and particle aspect ratios.

NIR Imaging

FIGS. 3 and 4, respectively, show a digital macro brightfield image anda raw NIR microscopic transmittance image of a CdZnTe semiconductormaterial with numerous tellurium inclusions. The left half of the waferhas been polished. The tellurium inclusions appear as dark spots in themicroscopic NIR image. The raw NIR microscopic image was acquired usingthe automated near-infrared tellurium inclusion volumetric mappingsystem.

Background Correction and Image Binarization

The automated particle analysis begins by applying a backgroundcorrection preprocessing routine to the raw image frames. One of thebiggest problems with the raw images collected is the gradually varyingbackground across each image frame. As a result, a particle in one areaof a frame may have a higher intensity value than the background ofanother area of that frame.

FIGS. 5A-5D illustrate the difficulty associated with selecting athreshold value for an image with a widely varying background. In FIGS.5A-5D, regions 1 and 2 have mean intensity values of approximately 2600and 1950, respectively. The whole of region 1 is primarily a particlewhereas region 2 is primarily background with a small particle in thecenter. FIG. 5A shows a raw NIR image frame collected from a singleregion of interest in a CdZnTe wafer. At wavelengths longer thanapproximately 850 nm, CdZnTe is transparent while tellurium inclusionsremain opaque. A NIR image of the sample is light where there are noprecipitates and dark where there are precipitates. In FIG. 5B, thethreshold value is set low enough (value=1520) that the particle inregion 2 is correctly identified, but most of the remaining particlesare not found. In FIG. 5C, the threshold value is set high enough(value=2470) so that all particles are detected. Unfortunately, a largearea of the frame is incorrectly identified as one very large particle.FIG. 5D displays the case in which the threshold is set to anintermediate value (value=1960). Many of the particles are correctlyidentified, but the particle in region 2 is identified as being largerthan it actually is.

To address this issue, a background correction step is used to force thebackground to be essentially constant across a given image frame. Theprocedure applies a moving window across the image frame and smoothesthe resulting background before subtracting it from the frame. Otheroperations such as low pass filtering and selective removal of badcamera pixels are also applied.

The second step in the automated particle analysis is the selection ofthe threshold value resulting in the binarized image which best reflectsthe number and size of particles actually present in the sample beingimaged. A human operator would typically approach this problem by tryingmultiple threshold values and comparing the resulting binarized imagesto the actual image to see which binarized image best matches theirperception of the particles in the actual image. The algorithm employedby the NIR chemical imaging microscope system takes essentially the sameapproach. A series of threshold values are used to generate binarizedimages. Each binarized image is submitted to a routine that finds theparticles present in the image. A set of particle morphology rules wasdeveloped to determine the point at which the threshold value identifiesthe particles consistent with results obtained by a trained humanoperator. This threshold value is then further refined with usingderivative operations.

FIGS. 6A-6C show montage views of raw, background-corrected, andbinarized NIR image frames, respectively, corresponding to four adjacentregions of interest from a CdZnTe wafer. A visual inspection of theseimages suggests that the particle analysis adequately identifies theparticles in an automated fashion.

Volumetric Reconstruction and Visualization

It is of particular interest to the semiconductor manufacturing industryto view defects, including tellurium inclusions in this example, in athree dimensional volumetric view. Individual binarized image framesgenerated at discrete axial planes of focus have been reconstructed intoa volumetric view allowing users to view tellurium inclusions inthree-dimensional space.

FIG. 7 shows a 3D volumetric view of tellurium inclusions in CdZnTegenerated from 50 individual image slices. FIG. 7 is constructed using anearest neighbors computational approach for volume reconstruction.Improved results can be obtained using more sophisticated strategiesthat deconvolve the entire image volume using iterative deconvolutionapproaches. The staring time of the sensor used to gather the volumetricdata was less than 1 sec. The total acquisition time for the datagenerated in this figure was well under a minute. Note how theinclusions tend to form in planes described as veils. These veils arebelieved to be subgrain boundaries within the CdZnTe material. Grainboundaries provide low energy nucleation sites for the inclusions toform during the growth process.

Table 1 provides tabulated statistical information on the volumetricdata shown in FIG. 7.

TABLE 1 Particle Statistics Slice Number and Depth (μm) Parameters 0 (0)10 (89.77) 20 (189.52) 30 (289.26) 40 (389.01) 50 (488.75) # ofInclusions 25 30 27 24 25 36 Mean Diameter (μm) 12.12 11.38 12.75 15.7012.89 13.73 Density (Inclusions/cm²) 4368 5241 4717 4193 4368 6289 Area(μm²) 97.48 73.78 91.67 119.25 96.29 98.15 Perimeter (μm) 40.40 37.3243.27 50.72 41.93 43.98 Shape Factor 0.60 0.60 0.58 0.53 0.60 0.55Maximum Chord Length 12.12 11.38 12.75 15.70 12.89 13.73 Feret 1Diameter 9.17 9.56 11.33 12.64 10.48 10.16 Feret 2 Diameter 10.26 9.0110.10 12.18 10.37 11.60 Aspect Ratio 1.02 1.19 1.16 1.08 1.02 0.95

Defects such as tellurium inclusions affect the electrical properties inCdZnTe semiconductor materials, degrading end-product deviceperformance. Having the ability to rapidly and non-invasively identifyand quantify tellurium inclusion defects at critical stages in thefabrication process provides semiconductor manufacturers withinformation that will enable them to optimize the manufacturing processand reduce production costs. The Automated NIR Volumetric Mapping Systemdescribed here is capable of providing such information. The systemprovides qualitative and quantitative information about telluriuminclusions present in CdZnTe wafers in two and three spatial dimensions.This system boasts improved spatial resolution (˜0.5 μm) compared tosystems currently used by many semiconductor manufacturers and itvirtually eliminates the subjectivity associated with human counting andsizing measurements. Whole wafers are capable of being characterized inminutes.

While in the above example, the present invention has been demonstratedin connection with the characterization of semiconductors, it is to beexpressly understood that the present invention can also be used in thecharacterization of other materials including, but not limited to, foodand agricultural products, paper products, pharmaceutical materials,polymers, thin films and in medical uses.

Although present preferred embodiments of the invention have been shownand described, it should be distinctly understood that the invention isnot limited thereto but may be variously embodied within the scope ofthe following claims.

We claim:
 1. A near infrared radiation chemical imaging systemcomprising: a) an illumination source for illuminating an area of asample using light in the near infrared radiation wavelength; b) adevice for collecting a spectrum of near infrared wavelength radiationlight transmitted, reflected, emitted or scattered from said illuminatedarea of said sample and producing a collimated beam therefrom; c) a nearinfrared imaging spectrometer for selecting a near infrared radiationimage of said collimated beam; and d) a detector for collecting saidfiltered near infrared images.
 2. The system of claim 1 wherein saidillumination source is one of a quartz tungsten halogen lamp, a tunablelaser, a metal halide lamp, and a xenon arc lamp.
 3. The system of claim1 wherein said device for collecting is one of a refractive typeinfinity-corrected near infrared optimized microscope objective, arefractive fixed tube length microscope objective, and a reflectingmicroscope objective.
 4. The system of claim 1 wherein said nearinfrared imaging spectrometer is selected from the group consisting ofLyot liquid crystal tunable filters; Evans Split-Element liquid crystaltunable filters; Solc liquid crystal tunable filters; Ferroelectricliquid crystal tunable filters; Liquid crystal Fabry Perot filters; ahybrid filter formed from a combination of liquid crystal tunablefilters; and a combination of a liquid crystal tunable filter and afixed bandpass and bandreject filters.
 5. The system of claim 1 whereinsaid detector is a near infrared radiation focal plane array detector.6. The system of claim 5 wherein said detector is selected from thegroup consisting of indium gallium arsenide, platinum silicide, indiumantimonide, palladium silicide, indium germanide, and mercury cadmiumtelluride.
 7. The system of claim 1 further comprising a visiblewavelength imagery system.
 8. The system of claim 7 wherein said visibleimagery system comprises: a) an illumination source for illuminating anarea of said sample using light in the visible optical wavelengths; andb) a device for detecting said visible wavelength light from saidilluminated area of said sample.
 9. The system of claim 8 wherein saiddevice for detecting said visible wavelength light comprises an analogand digital detector based on at least one of a silicon charge-coupleddevice detector and a silicon CMOS detectors.
 10. The system of claim 8further comprising a processor for producing a near infrared radiationchemical image of said sample.
 11. The system of claim 8 furthercomprising an algorithm for combining the near infrared and visibleimage data.
 12. A chemical imaging system comprising: a) an illuminationsource for illuminating an area of a sample using light in the nearinfrared radiation wavelength and light in the visible wavelength; b) adevice for collecting a spectrum of near infrared wavelength radiationlight transmitted, reflected, emitted or scattered from said illuminatedarea of said sample and producing a collimated beam therefrom; c) a nearinfrared imaging spectrometer for selecting a near infrared radiationimage of said collimated beam; d) detector for collecting said filterednear infrared images; and e) a device for detecting said visiblewavelength light from said illuminated area of said sample.
 13. Achemical imaging method comprising the steps of: a) illuminating an areaof a sample using light in the near infrared radiation wavelength andlight in the visible wavelength; b) collecting a spectrum of nearinfrared wavelength radiation light transmitted, reflected, emitted orscattered from said illuminated area of said sample and producing acollimated beam therefrom; c) filtering said collimated beam to producea near infrared radiation image of said collimated beam whilesimultaneously detecting said optical wavelength light from saidilluminated area of said sample; d) collecting said filtered nearinfrared images; and e) processing said collected near infrared imagesto produce a chemical image of said sample.
 14. A method for producing avolumetric image of a sample comprising the steps of: a) incorporating arefractive image formation optic exhibiting a chromatic response in theoptical path of the microscope before the near infrared detector; b)collecting images of said sample at a plurality of near infraredwavelengths through said objective at a fixed focus condition; and c)processing said collected images to reconstruct a depth resolved imageof said sample.
 15. A method for chemically analyzing a samplecomprising the steps of: a) seeding said sample with a plurality ofanalytes having at least one of a known composition, structure andconcentration; b) collecting a plurality of spatially-resolved spectrafor said plurality of analytes; c) producing a plurality of chemicalimages of said sample containing said plurality of anayltes; and d)processing said plurality of chemical images to generate a chemicalimage of said sample.
 16. The method of claim 15 wherein said processingstep comprises at least one of: a) correcting the image by dividing anear infrared image of said sample by a near infrared image of abackground of said image to produce a resulting ratioed image; b)normalizing the divided image by dividing each intensity value at everypixel in the image by the vector norm for its corresponding pixelspectrum, said vector norm being the square root of the sum of thesquares of pixel intensity values for each pixel spectrum; c) processingsaid image using a cosine correlation analysis method wherein each pixelspectrum is treated as a projected vector in n-dimensional space,wherein n is the number of wavelengths sampled in the image; and d)processing said image using a principal component analysis methodwherein a least squares fit is drawn through the maximum variance in then-dimensional dataset.